Preoperative prediction of the efficacy of neoadjuvant immunotherapy (NIT) in non-small cell lung cancer (NSCLC) helps identify patients who are likely to benefit, reduce the risk of postoperative recurrence and metastasis, and improve prognosis. Radiomics and deep learning can be used to explore imaging biomarkers for predicting NIT efficacy in NSCLC. Radiomics, through global feature analysis or habitat analysis methods, can effectively quantify the temporal and spatial heterogeneity of tumors, providing a quantitative basis for efficacy prediction. Deep learning, on the other hand, adaptively extracts deep imaging features to evaluate treatment response. This review summarizes recent research progress in radiomics and deep learning technologies for predicting NIT efficacy in NSCLC patients, and discusses the associated technical challenges and corresponding solutions.

Habitat imaging(HI) can analyze tumor heterogeneity and microenvironmental characteristics and has been increasingly applied in the research, diagnosis, and treatment of common digestive system tumors, including colorectal cancer, gastric cancer, and hepatocellular carcinoma. Currently, HI is used to construct genotypic prediction models, precision staging, and metastasis prediction in colorectal cancer; to quantify immune microenvironment characteristics, evaluate treatment response, and predict prognosis in gastric cancer; and to achieve non-invasive identification of microvascular invasion and recurrence risk stratification in hepatocellular carcinoma. This article introduces the basic principles and technical processes of HI, and reviews its research progress in the above-mentioned digestive system tumors.

Objective To investigate the association between the pericoronary adipose tissue fat attenuation index (FAI) surrounding culprit plaques in acute coronary syndrome (ACS) and plaque characteristics, and to assess its value in predicting culprit plaques. Methods This retrospective study enrolled 50 patients diagnosed with ACS (ACS group) and 40 asymptomatic individuals with coronary atherosclerosis who underwent coronary computed tomography angiography (CCTA) during the same period (control group). Clinical and imaging data were analyzed. In the ACS group, plaques were classified as culprit or non-culprit plaques. Based on the number of high-risk features, plaques were further categorized as non-high-risk or high-risk. FAI surrounding plaques was measured using predefined default (-190 to -30 HU) and wide (-190 to 20 HU) attenuation thresholds. Student’s t-test, one-way ANOVA, and chi-square test were used to compare FAI values of plaques with different characteristics and degrees of stenosis between and within groups; the plaque characteristics, stenosis severity, and FAI among culprit plaques, non-culprit plaques, and control group plaques; the high-risk features between culprit and non-culprit plaques; and the FAI values between high-risk and non-high-risk plaques. Multivariable logistic regression analysis was performed to identify independent predictors of culprit plaques. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of individual and combined factors for culprit plaques. The DeLong test was used to compare the differences in the area under the curve (AUC) among individual and combined factors. Results The FAI measured with the wide threshold was significantly higher than that measured with the default threshold for culprit plaques, non-culprit plaques, and control group plaques (all P<0.05). Under both thresholds, the FAI of culprit plaques was significantly greater than that of non-culprit plaques and control plaques (all P<0.05). Among the culprit plaques, 64% were classified as high-risk plaques, and these also showed high proportions of mixed plaque morphology, severe stenosis, and occlusion (52%, 76%, and 12%, respectively). In the ACS group, the FAI surrounding calcified plaques was lower than that surrounding non-calcified and mixed plaques (P<0.05). The FAI was significantly higher around plaques causing severe stenosis or occlusion (P<0.05), and higher around high-risk plaques compared to non-high-risk plaques (P<0.05). Multivariable logistic regression analysis indicated that stenosis severity ≥ moderate, higher default threshold FAI, and a greater number of high-risk plaque features were independent predictors of culprit plaques. The combination of default threshold FAI, stenosis severity, and high-risk features yielded the highest predictive performance (AUC=0.981). DeLong test analysis showed that the AUCs of models combining default threshold FAI with other factors were significantly higher than those of any single factor alone (all P<0.05). Conclusion The FAI surrounding ACS plaques can partially reflect plaque inflammation and vulnerability. Combining default threshold FAI with stenosis severity and high-risk features improves diagnostic performance in identifying culprit plaques.

Temporomandibular disorders (TMD) are common in dentistry, with complex etiologies and diverse clinical manifestations. Imaging techniques allow visualization and precise quantification of temporomandibular joint (TMJ) structures. MRI can reveal soft tissue abnormalities by analyzing disc morphology, degree of displacement, joint effusion, and lateral pterygoid muscle attachment patterns; while CT provides quantitative parameters of bony structures such as condylar morphology, size, and position. This review summarizes the associations between CT and MRI structural features of the TMJ with TMD severity and prognosis, aiming to provide a theoretical basis for precision diagnosis and treatment of TMD.

Neurovascular coupling (NVC) refers to the brain's regulatory capacity to adjust cerebral blood flow in response to neuronal activity, representing a coordinated function between neural activity and hemodynamics. Impairment in NVC diminishes connectivity between distinct brain regions, thereby impairing cognitive function. Currently, various neuroimaging techniques including arterial spin labeling, resting-state functional MRI, and functional near-infrared spectroscopy are employed to evaluate NVC function. These methods have confirmed that declines in NVC are closely linked to cognitive impairments in conditions such as vascular cognitive impairment, Alzheimer's disease, diabetic cognitive decline, and cognitive deficits in end-stage renal disease patients. This review aims to provide an overview of recent advances in neuroimaging research on the relationship between NVC and cognitive disorders.

Musculoskeletal disorders are characterized by diverse pathological types and complex anatomical structures, placing high demands on the precision of imaging examinations. In recent years, artificial intelligence (AI) has shown great potential in musculoskeletal imaging, particularly in anatomical segmentation, lesion detection, quantitative measurement, and intelligent diagnosis. This review systematically summarizes advances in AI applications for degenerative joint diseases, sports injuries, fracture detection, osteoporosis screening, and musculoskeletal tumors, while also outlining its expanding roles in image reconstruction, quality control, and educational support. Furthermore, it highlights the developmental trends of large AI models in musculoskeletal imaging and discusses their potential and challenges in multimodal, multitask, and personalized clinical decision support. Although AI has reached or even surpassed expert-level performance in certain tasks, limitations remain in model generalization, data acquisition and annotation standards, cross-modality integration, and clinical adaptability. Future progress will require high-quality data construction, interdisciplinary collaboration, and the establishment of standardized frameworks to advance musculoskeletal AI toward more intelligent, efficient, and standardized clinical practice.

Objective To evaluate the value of CT features of pulmonary ground-glass nodules (GGNs) in predicting the invasiveness and invasion degree of lung adenocarcinoma. Methods A retrospective study was conducted on 168 postoperative patients with isolated GGN on chest CT and complete pathological results from surgery or biopsy. Based on whether the lesion had invasive components, patients were divided into a non-invasive group (44 cases) and an invasive group (124 cases). The invasive group was further subdivided into minimally invasive adenocarcinoma group (MIA group, 61 cases) and invasive adenocarcinoma group (IAC group, 63 cases) according to the degree of invasion. CT features of the GGNs were analyzed, including long diameter, short diameter, mean CT value, and proportion of ground-glass opacity (GGO). The intraclass correlation coefficient (ICC) was used to assess interobserver agreement between two radiologists. Independent sample t-tests, Mann-Whitney U tests, and chi-square tests were used to compare CT features between groups. CT features with statistically significant differences were included in multivariate logistic regression analysis to identify independent predictors of invasiveness and invasion degree. Receiver operating characteristic (ROC) curves were used to analyze the predictive performance of independent and combined predictors. Results The measurement consistency measurements of GGN long diameter, short diameter, GGO proportion, and mean CT value between the two physicians was good (all ICC>0.9). Significant differences in the presence of spiculation, lobulation, vascular change, pleural retraction, shape, GGN type, long diameter, short diameter, mean CT value, and GGO proportion were observed between the non-invasive and invasive groups (all P<0.05). Among these, spiculation, mixed GGN (mGGN), and long diameter were independent risk factors for predicting GGN invasiveness (all P<0.05). Significant differences in lobulation, vascular change, vacuole sign, density uniformity, long diameter, short diameter, mean CT value, and GGO proportion were found between the MIA and IAC groups (all P<0.05). Among these, type Ⅱ vascular change, GGN long diameter, short diameter, and mean CT value were independent risk factors for predicting the degree of invasion (all P<0.05). Among single predictors of invasiveness, long diameter had the highest AUC (0.818); the combined predictor model had an AUC of 0.885, higher than any single predictor. For invasion degree prediction, short diameter had the highest AUC (0.896); the combined predictor model had an AUC of 0.945, again higher than any single factor. Conclusion A combined assessment of multiple CT imaging features of GGNs can improve the prediction of both the invasiveness and degree of invasion in lung adenocarcinoma, providing stronger radiological evidence for individualized clinical diagnosis and treatment planning.

Ultrasound technology offers clear visualization of skin tissue structure, echogenicity, and blood flow, while also providing information on tissue stiffness. It enables measurement of lesion depth and facilitates the assessment of recurrence and treatment efficacy. Currently, its applications in the diagnosis and evaluation of non-mass skin diseases involve modalities such as high-frequency ultrasound (HFUS), color Doppler ultrasound, and ultrasound elastography. This review summarizes recent advancements in applying ultrasound technology to non-mass skin conditions, including connective tissue diseases involving the skin, bullous diseases, cellulitis, other inflammatory dermatoses, and pressure injuries.

Neoadjuvant chemotherapy (NAC) can not only reduce the stage of breast cancer but also enable some tumor lesions and axillary lymph nodes to achieve pathological complete response (pCR). Accurate preoperative imaging assessment of axillary lymph node status after NAC in breast cancer patients can help avoid excessive surgical intervention and guide the development of individualized treatment plans. This review summarizes recent progress in the use of imaging methods such as ultrasound, MRI, CT, and PET/CT to evaluate axillary lymph node pCR after NAC.

Objective To investigate differences in white matter microstructure between patients with neuropsy-chiatric systemic lupus erythematosus (NPSLE) and those with non-neuropsychiatric SLE (Non-NPSLE) using tract-based spatial statistics (TBSS). Methods A total of 34 NPSLE patients and 32 Non-NPSLE patients were prospectively enrolled, along with 33 healthy controls (HC) during the same period. All participants underwent brain diffusion tensor imaging (DTI). TBSS was used to compare white matter microstructural differences among the three groups. Fractional anisotropy (FA) values were compared using one-way ANOVA, with post hoc analyses conducted for pairwise group comparisons. Partial correlation analyses assessed the relationships between FA values of significantly different clusters and neuropsychological scores or clinical indicators, as well as the correlations between neuropsychological scores and clinical indicators. Results Five clusters showed significant FA differences among the three groups (P<0.05, FWE-corrected). Post hoc analysis revealed that two clusters in both the Non-NPSLE and NPSLE groups had lower FA values than the HC group, and one cluster in the NPSLE group had a lower FA value than the Non-NPSLE group (P<0.05, FWE-corrected), indicating more extensive white matter involvement in NPSLE. FA reductions in SLE patients were primarily located in the corpus callosum and corona radiata. Correlation analysis showed that FA values of the significant clusters in pairwise comparisons were positively correlated with IgM levels (P<0.05). In the NPSLE group, HADS-D scores were negatively correlated with C4 levels (r=-0.354, P=0.047), while in the Non-NPSLE group, MoCA scores were negatively correlated with ESR (r=-0.424, P=0.019). Conclusion NPSLE patients exhibit more extensive white matter microstructural damage compared to Non-NPSLE patients. The FA values of some differential clusters correlate with clinical indicators, suggesting that these changes may serve as important imaging biomarkers for detecting disease activity or neuropsychiatric involvement in SLE.

Objective To investigate the relationship between pericoronary fat attenuation index (FAI) and the plaque progression. Methods This retrospective study included 140 inpatients with suspected coronary artery disease (CAD) who underwent consecutive coronary computed tomography angiography (CCTA), with an average age of 56.8 ± 10.5 years old. A total of 348 plaques were identified. Clinical data, as well as baseline and follow-up (at least one year apart) CCTA imaging data, were collected. Characteristics, degree of stenosis, plaque volume (PV), and percentage atheroma volume (PAV) were analyzed. Peri-plaque FAI was measured and analyzed. A multivariate generalized estimating equation was used to adjust for confounding variables, and linear regression models were fitted to analyze the association between the annual change in FAI (ΔFAI/y) and the annual changes in PV (ΔPV/y) and PAV (ΔPAV/y). Results The median interval between the two CCTA scans was 2.3 (1.7, 3.8) years. Quantitative analysis of the two scans revealed significant increases in all components of PV and PAV except for lipid PV and lipid PAV (all P<0.001). After adjusting for China-PAR score, Leiden score, TyG index, antiplatelet therapy, and statin use using generalized estimating equation, ΔPAV/y of total plaques was significantly positively correlated with ΔFAI/y (β=0.156, 95%CI: 0.025-0.287, P=0.019). Specifically, ΔPAV/y of non-calcified plaques (β=0.139, 95%CI: 0.006-0.273, P=0.041) and fibrous plaques (β=0.197, 95%CI: 0.067-0.327, P=0.003) also showed significant positive correlations with ΔFAI/y. Conclusion Changes in FAI are consistent with changes in non-calcified PAV and fibrous PAV. This may help identify potentially high-risk patients with stable coronary artery disease and supports the use of FAI as a valuable tool for evaluating treatment efficacy in coronary artery disease.

Cardiovascular imaging techniques play a crucial role in the diagnosis, treatment, and prognosis of cardiovascular diseases. In 2024, Chinese scholars achieved remarkable research outcomes in this field, with findings published in various high-level domestic and international journals. By systematically reviewing the guidelines and expert consensuses, technological advancements, clinical studies, and artificial intelligence-related studies published by Chinese scholars in 2024, this review summarizes the key research hotspots and directions, aiming to provide reference and guidance for clinicians in both diagnostic decision-making and clinical research endeavors.

Vulnerable carotid plaques and cerebral small vessel disease (CSVD) are both significant etiologies of ischemic stroke, and they are closely interrelated. Imaging modalities, including ultrasound, CT angiography (CTA), and MR high-resolution vessel wall imaging (HR-VWI), enable the assessment of various vulnerable plaque characteristics. This review focuses on the correlation between vulnerable plaques and CSVD, aiming to provide a theoretical basis for further research and clinical application.

Ischemia and hypoxia caused by cerebral vascular stenosis or occlusion constitute the core pathological mechanisms of ischemic stroke. As an important compensatory pathway for blood supply in the body, collateral circulation directly influences the survival time of the ischemic penumbra, the rate of infarct expansion, and the long-term prognosis of patients. This article reviews and analyzes the classification of cerebral collateral circulation and its related hemodynamic basis, CT and MRI-based evaluation of collateral circulation, as well as the pathological basis of collateral circulation in ischemic stroke and its correlation with imaging findings. It is anticipated that future imaging evaluation of collateral circulation will develop toward greater precision, multimodality integration, and intelligence, thereby providing stronger support for individualized patient treatment.

Objective To investigate the predictive value of the minimum distance between left atrial appendage (LAA) and left superior pulmonary vein (LSPV), measured on cardiac computed tomography (CT), for atrial fibrillation (AF) recurrence within 2 years after the first radiofrequency ablation procedure. Methods A retrospective analysis was conducted on the clinical and imaging data of 342 AF patients who underwent cardiac CT, with a median age of 64 (56, 71) years. Based on recurrence status within 2 years, patients were divided into a recurrence group (n=106) and a non-recurrence group (n=236). Differences in clinical and cardiac CT parameters between the two groups were analyzed using the chi-square test or Mann-Whitney U test. Cox regression analysis was used to identify independent predictors of AF recurrence and construct predictive models. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate model performance, and DeLong’s test was used to compare AUC values. Kaplan-Meier analysis was used to assess event-free survival, and the log-rank test was used to compare survival between groups. Results The proportion of patients with an end-diastolic LAA-LSPV distance (LAA-LSPV_{end-diastolic}) <2 mm was significantly higher in the recurrence group than in the non-recurrence group (P<0.05). The recurrence group also had significantly higher end-diastolic and end-systolic volume indices of the left atrium and LAA (LAVImax, LAVImin, LAAVImax, LAAVImin) compared to the non-recurrence group (all P<0.05). Multivariate Cox regression identified (LAA-LSPV_{end-diastolic}) <2 mm, LAAVImax, persistent AF, and NT-proBNP as independent predictors of recurrence. Three predictive models were developed: Model 1: clinical parameters only (persistent AF+NT-proBNP); Model 2: Model 1+LAAVImax; Model 3: Model 2+LAA-LSPV_{end-diastolic} <2 mm. Model 3 had significantly better predictive performance than both Model 1 (Z=2.829, P<0.05) and Model 2 (Z=2.246, P<0.05). Conclusion LAA-LSPV_{end-diastolic} <2 mm is an independent predictor of AF recurrence within 2 years after ablation and provides incremental prognostic value.

Objective To investigate a model based on MRI habitat imaging analysis of axillary lymph node (ALN) heterogeneity combined with clinicopathological features for assessing the response to neoadjuvant therapy (NAT) in breast cancer patients with ALN positivity (ALN+). Methods A total of 369 female patients with pathologically confirmed ALN+ breast cancer were retrospectively enrolled and randomly divided into a training set (n=259) and a validation set (n=110) in a 7∶3 ratio. All patients underwent dynamic contrast-enhanced MRI before treatment. Stable 3D radiomic features were used, and the optimal number of clusters was determined using Gaussian mixture modeling and the Bayesian information criterion to generate habitat imaging and extract subregional features. Four support vector machine (SVM)-based models were constructed: a clinical model, a habitat radiomics model, an ALN heterogeneity score model, and a late-fusion combined model. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared using the Delong test. Clinical utility was assessed using decision curve analysis. Results The clinical model was constructed based on PR status, HER2 status, and Ki-67 index. The habitat radiomics model was developed using 17 non-zero radiomic features extracted from four subregions. An ALN heterogeneity score model and a combined model were also established. In the validation set, the AUCs of the clinical model, habitat radiomics model, ALN heterogeneity score model, and combined model were 0.79, 0.60, 0.69, 0.85, respectively. The Delong test showed that the AUC of the combined model was significantly higher than those of all individual models (all P<0.05). Decision curve analysis demonstrated that the combined model consistently provided a high net benefit within the threshold probability range of 0.2-0.8, indicating favorable clinical applicability. Conclusion A combined model integrating MRI-based habitat radiomics features, ALN heterogeneity scores, and clinicopathological characteristics may assist in evaluating post-NAT lymph node status and support individualized clinical decision-making.

The imaging diagnosis of focal fat-containing hepatic poses a challenge due to the heterogeneity of fat components within the lesions and the overlap of imaging features. This article systematically analyzed the imaging differences among steatosis, adipose tissue, and liquid fat. It summarized the imaging features of focal fat-containing liver lesions such as hepatocellular carcinoma, hepatocellular adenoma, and angiomyolipoma. By identifying the specific type of fat within the lesion, accurately assessing other imaging features (e.g., enhancement patterns), and integrating relevant clinical information, the differential diagnosis can be narrowed, thereby improving diagnostic accuracy.

Ischemia with non-obstructive coronary artery disease (INOCA) is characterized by coronary microvascular dysfunction and epicardial vasospasm as its core pathological mechanisms, which significantly increase the risk of adverse cardiovascular events. Non-invasive imaging modalities, including cardiac magnetic resonance imaging, myocardial computed tomography perfusion imaging, echocardiography, and positron emission tomography, have demonstrated significant potential in the evaluation of INOCA. Artificial intelligence further enhances the efficiency and accuracy of imaging-based assessments. This article provides a systematic review of the pathophysiological mechanisms underlying INOCA and recent advances in imaging techniques for its evaluation, with a focus on clinical applicability and technological innovation.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by insidious early symptoms, rapid progression, and extremely poor prognosis. The Chinese Guidelines for the Diagnosis and Treatment of Pancreatic Cancer (2022 Edition) emphasize the importance of early screening and precision imaging assessment in high-risk populations, and recommend MRI as an important modality for screening and diagnosis. In conjunction with the epidemiological characteristics of PDAC in China and the key points of domestic and international expert consensus, this article systematically interprets the latest international MRI screening protocols and reporting templates. The aim is to improve the early detection rate of PDAC, improve patient prognosis, and provide evidence-based support for the continuous refinement of domestic guidelines.

Hepatocellular carcinoma (HCC) is a common malignant tumor, and early diagnosis and treatment are crucial. Contrast-enhanced ultrasound (CEUS) enables real-time observation and quantitative analysis of blood perfusion in lesion tissues, which helps improve the diagnostic accuracy and the ability to differentiate between HCC subtypes. It also provides effective support for personalized treatment. At present, CEUS has been gradually applied in various aspects of HCC management, including the diagnosis of different subtypes, personalized treatment planning, evaluation of therapeutic efficacy and prognosis, and molecular targeted diagnosis and treatment. This article reviews the current status and recent progress in the application of CEUS in the diagnosis of HCC subtypes and personalized treatment.

Objective To evaluate the value of models based on clinical and MRI features in predicting HER-2-positive and HER-2-low expression breast cancers. Methods A retrospective analysis was conducted on 213 female patients (mean age 50.8±10.6 years) with surgically and pathologically confirmed mass-forming breast cancer. Based on postoperative pathological results, patients were categorized into HER-2-zero (65 cases), HER-2-low (79 cases), and HER-2-positive (69 cases) groups. Clinical and MRI characteristics were compared among the three HER-2 expression groups using one-way ANOVA and chi-square tests, including estrogen receptor (ER) status, progesterone receptor (PR) status, T stage, clinical stage, maximum lesion diameter, and apparent diffusion coefficient (ADC) values. Multivariate logistic regression was used to identify independent predictive factors for the HER-2-positive and HER-2-low expression subtypes, followed by predictive model construction. Receiver operating characteristic (ROC) analysis was used to assess model performance. Results Significant differences were observed among the HER-2-zero, HER-2-low, and HER-2-positive groups in ER status, PR status, T stage, clinical stage, maximum lesion diameter, and ADC values (all P<0.05). Multivariate analysis demonstrated that clinical stage and ADC value were independent predictors for both HER-2-positive and HER-2-low breast cancer (both P<0.05). The model constructed using clinical stage and ADC demonstrated high predictive efficacy for both HER-2 positive and HER-2-low expressing breast cancers, with areas under the curve (AUC) of 0.899 and 0.861, respectively. Conclusion A model integrating clinical stage and ADC values shows high efficacy for the noninvasive prediction of HER-2-positive and HER-2-low expression breast cancers.

Objective To investigate the characteristics of altered spontaneous brain activity in patients with sensorineural hearing loss (SNHL) using activation likelihood estimation (ALE) meta-analysis, and to further understand the potential neural mechanisms of brain functional impairment and remodeling in SNHL. Methods A systematic search was conducted up to August 21, 2023, in Web of Science, PubMed, CNKI, Wanfang Med Online, and the Chinese Medical Journal Full-text Database for studies applying regional homogeneity (ReHo) and amplitude of low-frequency fluctuation/fractional ALFF (ALFF/fALFF) analyses to evaluate brain functional changes in SNHL patients. After applying inclusion and exclusion criteria, studies were included in a meta-analysis using the ALE method to identify brain regions with abnormal spontaneous neural activity in SNHL patients. Results A total of 22 articles comprising 29 studies were included, involving 736 cases of SNHL and 487 controls. Among these, 11 studies used ReHo and 18 studies used ALFF/fALFF. A combined analysis of ReHo/ALFF/fALFF without differentiating the side of deafness revealed increased spontaneous brain activity in the left medial dorsal thalamus, and decreased activity in the left superior temporal gyrus, left opercular part of the inferior frontal gyrus, and left dorsolateral prefrontal cortex. Separate ALE analyses for left-sided and right-sided SNHL patients, as well as separate analyses using ReHo or ALFF/fALFF methods, did not identify any significantly altered brain regions. Conclusion The ALE meta-analysis confirms that SNHL patients exhibit abnormal spontaneous activity in multiple brain regions. These findings help elucidate the patterns and characteristics of brain functional damage and remodeling associated with SNHL, providing important evidence for future clinical assessment and treatment planning.

Objective To evaluate the setup accuracy of surface-guided radiotherapy (SGRT) in hypofractionated radiotherapy for breast cancer, to quantify the relationship between setup errors and target dose distribution, and to determine setup error control thresholds according to different planning target volume (PTV) sizes. Methods Sixty female patients with breast cancer who underwent breast-conserving surgery and completed hypofractionated radiotherapy were retrospectively enrolled. According to the treatment guidance method, patients were divided into a conventional setup group (n=30) and an SGRT group (n=30). Setup errors between the two groups were compared using the Mann-Whitney U test. Spearman correlation analysis was performed to assess the association between SGRT-derived setup errors and cone-beam computed tomography (CBCT) registration errors, followed by linear fitting using ordinary least squares regression. Dose variations caused by simulated setup errors were evaluated using the Eclipse treatment planning system. Pearson correlation analysis was applied to investigate the linear relationship between PTV volume and actual delivered dose under different setup error conditions. Results Setup errors along the X, Y, and Z axes in the SGRT group were significantly smaller than those in the conventional group (all P<0.05), and the three-dimensional setup errors of all SGRT cases were <0.4 cm. SGRT-derived setup errors showed a strong positive correlation with CBCT-based registration results (r=0.81-0.91, all P<0.001), demonstrating high consistency between the two methods. Dosimetric simulation revealed that when the overall setup error exceeded 0.4 cm, target dose deviations increased markedly, with the Z-axis showing the highest sensitivity. Significant axial differences were observed in the linear correlation between PTV volume and delivered dose. For the Z-axis, a moderate correlation was identified at setup errors of 0.4 cm and ±0.5 cm, and a weak correlation at -0.4 cm. For the X-axis, a weak correlation was observed at -0.4 cm and -0.5 cm, whereas no significant linear correlation was found for the Y-axis at any setup error level. Notably, in patients with PTV <400 cm⊃3;, the Z-axis setup error should be controlled within 0.2 cm. Conclusion SGRT provides accurate, safe, and non-invasive guidance for hypofractionated radiotherapy in breast cancer, reducing radiation exposure and patient psychological burden while improving workflow efficiency. Setup error thresholds along the Z-axis should be individualized based on PTV volume to ensure optimal dose delivery.

Transcranial magnetic stimulation (TMS) is a non-invasive physical therapy technique that has demonstrated clear efficacy in treating various neurological diseases. However, its therapeutic efficacy varies significantly between individuals, and precise localization of personalized stimulation targets is critical to improving efficacy. Structural MRI can guide TMS by accounting for individual differences in brain anatomy. Functional MRI can identify individualized coordinates of brain function and abnormal brain activity, providing personalized stimulation targets for TMS. Resting-state fMRI functional connectivity is believed to provide a “bridge”, enabling magnetic stimulation to propagate from the cortical surface to deep effect targets. This article introduces the progress of MRI in the precise localization of TMS targets.

Gastric cancer is one of the malignancies with high incidence and mortality worldwide. Neoadjuvant immunotherapy has significantly improved the prognosis patients with gastric cancer, and it is of great clinical significance to accurately evaluate the efficacy of neoadjuvant immunotherapy at an early stage. Conventional imaging modalities, such as CT and MRI, have demonstrated value in evaluating the efficacy of neoadjuvant immunotherapy. In addition, artificial intelligence models can be widely applied to predict responses to neoadjuvant immunotherapy in gastric cancer and have shown good performance. This article reviews the research progress in the evaluation of neoadjuvant immunotherapy efficacy using conventional imaging modalities and artificial intelligence models in gastric cancer.

Rheumatic diseases (RD) are a group of systemic disorders affecting multiple systems, with chronic inflammatory responses in vascular and connective tissues as their main pathological basis. Due to its sensitive detection of metabolic activity and systemic inflammatory burden, ¹⁸F-FDG PET/CT, has become increasingly important for early diagnosis, assessment of disease activity, and monitoring of treatment in RD. This article reviews the research progress of ¹⁸F-FDG PET/CT in various RD, including rheumatoid arthritis, polymyalgia rheumatica, idiopathic inflammatory myopathies, adult-onset Still’s disease, systemic lupus erythematosus, IgG4-related disease, relapsing polychondritis, Sjögren’s syndrome, spondyloarthritis, and Takayasu arteritis,etc.